Machine Learning MCQ - the role of non-linear activation functions in neural network

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, What is the role of non-linear activation functions in neural network? Activation functions Tanh, ReLU and Sigmoid helps to learn non-linear decision boundaries, Why do we need an activation function in neural network?

Machine Learning MCQ - What is the purpose of non-linear activation function in neural network?

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1. In neural networks, what is the role of nonlinear activation functions such as sigmoid, tanh, and ReLU?

a) They speed up the gradient calculation in backpropagation, as compared to linear units

b) They help to learn nonlinear decision boundaries

c) They always output values between 0 and 1

d) All of the above

Answer: (b) They help to learn nonlinear decision boundaries Non-linear activation functions such as ReLU, Tanh, and Sigmoid can help the network learn complex data, compute and learn almost any function representing a question, and provide accurate predictions. A neural network must be able to take any input from -infinity to +infinity, but it should be able to map it to an output that ranges between {0,1} or between {-1,1} in some cases - thus the need for activation function. Non-linearity is needed in activation functions because its aim in a neural network is to produce a nonlinear decision boundary via non-linear combinations of the weight and inputs.   What is an activation function? An activation function in a neural network defines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network. Activation functions in general are used to convert linear outputs of a neuron into nonlinear outputs, ensuring that a neural network can learn nonlinear behavior.   Why do we need an activation function in neural network? Without activation function, weight and bias would only have a linear transformation, or neural network is just a linear regression model, a linear equation is polynomial of one degree only which is simple to solve but limited in terms of ability to solve complex problems or higher degree polynomials.  On the other hand, with activation function added to neural network executes the non-linear transformation to input and make it capable to solve complex problems such as language translations and image classifications.   Why non-linear activation functions used in the hidden layer of neural network? Differentiable non-linear activation functions are used in the hidden layers of a neural network. This allows the model to learn more complex functions than a network trained using a linear activation function. This helps to get access to a much richer hypothesis space that would benefit from deep representations

 

  

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What is an activation function in neural network?

Why do we need an activation function in neural network

Purpose of activation functions ReLU, Sigmoid, and Tanh in neural network

How does activation function helps learning complex things through neural network?

Why non-linear activation functions used in the hidden layer of neural network?

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